A novel microarray denosing algorithm using spectral subtraction

A new adaptive signal-preserving technique for noise suppression in gene expression data is proposed based on spectral subtraction. The proposed technique estimates a parametric model for the power spectrum of random noise from the acquired data based on the characteristics of the Rician statistical model. The new technique is tested using computer simulations from DREAM3 competition dataset. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components in the signal. Also, this new method outperforms other denoising methods like multi-wavelet algorithm. Moreover, when the new technique is used given its simple form, the new method does not change the statistical characteristics of the signal or cause correlated noise to be present in the processed signal. This suggests the value of the new technique as a useful preprocessing step for gene expression data analysis.